I present work toward the development of Model Order Reduction (MOR) techniques to integrate (i) parameterized mathematical models, and (ii) experimental observations, for prediction of engineering Quantities of Interest (QOIs). More in detail, I present two Simulation-Based approaches — the PBDW approach to state estimation, and the SBC approach for damage identification — that map observations to accurate estimates of the QOI, without estimating the parameters of the model. PBDW and SBC rely on recent advances in MOR to speed up computations in the limit of many model evaluations, and/or to compress prior knowledge about the system coming from the parameterized model into low-dimensional and more manageable forms. In the last part of the talk, motivated by the extension of PBDW and SBC to Fluid Mechanics problems, I present a MOR technique for long-time integration of parameterized turbulent flows. The approach corrects the standard Galerkin formulation by incorporating prior information about the attractor, and relies on an a posteriori error indicator to estimate the error in mean flow prediction.